Combining Phylogeography with Distribution Modeling: Multiple Pleistocene Range Expansions in a Parthenogenetic Gecko from the Australian Arid Zone

Combining Phylogeography with Distribution Modeling:Multiple Pleistocene Range Expansions ina Parthenogenetic Gecko from the Australian Arid ZoneJared L. Strasburg1¤*, Michael Kearney2, Craig Moritz3, Alan R. Templeton1

1 Department of Biology, Washington University, St. Louis, Missouri, United States of America, 2 Department of Zoology and Centre for EnvironmentalStress and Adaptation Research, The University of Melbourne, Parkville, Victoria, Australia, 3 Museum of Vertebrate Zoology, University of California atBerkeley, Berkeley, California, United States of America

Phylogenetic and geographic evidence suggest that many parthenogenetic organisms have evolved recently and have spreadrapidly. These patterns play a critical role in our understanding of the relative merits of sexual versus asexual reproductivemodes, yet their interpretation is often hampered by a lack of detail. Here we present a detailed phylogeographic study ofa vertebrate parthenogen, the Australian gecko Heteronotia binoei, in combination with statistical and biophysical modelingof its distribution during the last glacial maximum. Parthenogenetic H. binoei occur in the Australian arid zone and have thewidest range of any known vertebrate parthenogen. They are broadly sympatric with their sexual counterparts, from whichthey arose via hybridization. We have applied nested clade phylogeographic, effective migration, and mismatch distributionanalyses to mitochondrial DNA (mtDNA) sequences obtained for 319 individuals sampled throughout the known geographicranges of two parthenogenetic mitochondrial lineages. These analyses provide strong evidence for past range expansionevents from west to east across the arid zone, and for continuing eastward range expansion. Parthenogen formation and rangeexpansion events date to the late Pleistocene, with one lineage expanding from the northwest of its present range around240,000 years ago and the second lineage expanding from the far west around 70,000 years ago. Statistical and biophysicaldistribution models support these inferences of recent range expansion, with suitable climatic conditions during the lastglacial maximum most likely limited to parts of the arid zone north and west of much of the current ranges of these lineages.Combination of phylogeographic analyses and distribution modeling allowed considerably stronger inferences of the historyof this complex than either would in isolation, illustrating the power of combining complementary analytical approaches.

Citation: Strasburg JL, Kearney M, Moritz C, Templeton AR (2007) Combining Phylogeography with Distribution Modeling: Multiple PleistoceneRange Expansions in a Parthenogenetic Gecko from the Australian Arid Zone. PLoS ONE 2(8): e760. doi:10.1371/journal.pone.0000760

INTRODUCTIONAll vertebrate parthenogenetic lineages examined in any detail

have been found to be quite young in evolutionary terms, typically

being no more than one million years old and often much younger

[1]. Recent origins are also suggested by the ‘twiggy’ taxonomic

distribution of parthenogenetic organisms [2–4], which are

taxonomically widespread but extremely ‘species’ poor within

any given lineage [with very few exceptions–see 5]. Despite the

apparently limited life-spans of most parthenogenetic lineages,

they can potentially be very successful in the short term, as

evidenced by their often broad geographic distributions and by

molecular signatures of rapid range expansions [6–8]. Consider-

able effort has gone into explaining these patterns and their

implications for the importance of sexual reproduction in

evolution [4,9–12], but interpretations have often been hampered

by a lack of detailed phylogeographic data.

To properly understand the evolutionary dynamics of parthe-

nogenesis, it is necessary to compare the amount and distribution

of genetic variation in parthenogenetic lineages with that in closely

related sexual lineages [1]. This can allow the identification of

parental taxa [13] as well as provide information on the number of

clonal origins [14], the ages of clonal lineages [15], and the

proportion of genetic variation in parthenogens due to post-

formation mutation [16]. Recently developed molecular markers

and analytical techniques have allowed for more detailed and

informative genetic and phylogeographic comparisons between

sexual and asexual taxa [7,17–19]. In addition, combination of

phylogeographic approaches with analyses of ecological tolerances

and interactions can permit cross-validation of phylogeographic

inferences [20] and lead to considerably more insight into the

underlying processes that generate the observed patterns of

geographic distributions, amounts and distributions of genetic

variation, and ecological and climatic correlates [e.g. 21, 22, 23].

Here we present a detailed phylogeographic analysis of

parthenogenesis in a vertebrate, the Australian gecko Heteronotia

binoei. We then combine this with high-resolution statistical [24]

and biophysical [25] distribution models to make inferences of

their likely distributions during the last glacial maximum (LGM).

Parthenogenetic H. binoei have the largest range of any known

vertebrate parthenogen, including extensive areas where they

overlap with the ranges of their sexual counterparts. These

attributes make them an appealing subject for the study of

Academic Editor: Suzannah Rutherford, Fred Hutchinson Cancer Research Center,United States of America

Received May 12, 2007; Accepted July 13, 2007; Published August 22, 2007

Copyright: � 2007 Strasburg et al. This is an open-access article distributedunder the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.

Funding: This work was supported by a Howard Hughes Medical Institutepredoctoral fellowship to JLS and by grants from the Ethel Mary Read Fund, PeterRankin Trust Fund for Herpetology, and Australian Research Council to MK.

Competing Interests: The authors have declared that no competing interestsexist.

* To whom correspondence should be addressed. E-mail: [email protected]

¤ Current address: Department of Biology, Indiana University, Bloomington,Indiana, United States of America

PLoS ONE | www.plosone.org 1 August 2007 | Issue 8 | e760

adaptation and evolutionary success in parthenogens, and of

interactions between parthenogens and their parental taxa.

Heteronotia binoei is a complex of several diploid sexual chromosome

races and two mitochondrially distinct lineages of triploid

parthenogenetic clones that formed via hybridization between

two of the sexual chromosome races [26]. The CA6 and SM6

sexual chromosome races were involved in reciprocal hybridiza-

tion events giving rise to the 3N1 and 3N2 (so named because they

are triploid) parthenogenetic mtDNA lineages [27]. A third sexual

chromosome race, EA6, was not involved in the hybridization

events but is geographically widespread and sympatric with 3N1

parthenogens in part of its range. Numerous other sexual

chromosome races are much more geographically localized and

not as well characterized [26]. Parthenogenetic H. binoei exhibit

substantial nuclear genetic diversity within each mtDNA lineage,

mostly as a result of repeated backcrossing events between putative

diploid female hybrids and sexual males [16].

Considerable work has been done characterizing the sexual and

parthenogenetic taxa using cytology [28], allozymes [16], and

mtDNA restriction profiles [6]. Our recent detailed phylogeo-

graphic study of the three widespread sexual races, including the

two involved in the hybridization events [29], indicates that they

diversified approximately 6 million years ago and expanded into

the Australian arid zone during an extended period of gradual

aridification throughout much of continental Australia. We have

also presented molecular and distributional evidence that H. binoei

and an invertebrate from the Australian arid zone, the

grasshopper Warramaba virgo, have evolved hybrid parthenogenesis

in parallel and in a strikingly similar fashion, both geographically

and temporally [30]. Here we extend this work with an analysis of

the origin, spread, and current population structure of partheno-

genetic H. binoei using more powerful molecular markers and

coalescent-based population genetic techniques. We consider the

formation and expansion of the parthenogenetic lineages within

the context of the last few glacial cycles, in which glacial intervals

in much of continental Australia have been associated with

increased aridity [31]. In addition, we compare our results to

statistical [24] and biophysical [25] distribution analyses of the H.

binoei complex, and extend these analyses to consider climatic

conditions during the LGM. Our combined analyses allow for

robust descriptions of the formation and expansion of H. binoei

parthenogenetic lineages during the last two glacial cycles, and

they suggest further avenues of research into the evolutionary

dynamics of this complex.

RESULTSDNA sequences for all parthenogens ranged from 1283 to 1286

bases. Sequences were aligned manually, and at eight places gaps

of one to two bases were inserted to keep all sequences in

alignment. All indels occurred within or between adjacent tRNA

genes. The aligned DNA sequences consisted of 1289 characters.

Summary sequence diversity data for each lineage and for regions

within lineages are shown in Table 1.

Nested Clade Phylogeographic AnalysesHaplotype networks for the 3N1 and 3N2 mtDNA lineages are

shown in Figures 1 and 2, respectively. Geographic distributions of

the nesting clades are given in Table 1. In the 3N1 lineage, clade

2–1 is almost exclusively (81 of 82 individuals) restricted to eastern

populations, and clade 2–2 is mostly (133 of 148 individuals)

restricted to western populations. In the 3N2 lineage, clade 2–2 is

restricted to the northeastern part of the range, and clade 2–1 is

restricted to the rest of the range; clade 2–3 occurs in all but the

Far West region. Significant clade and nested clade distances and

NCPA inferences for the two lineages are given in Table 2. Clades

with nonsignificant distance values or for which the interpretation

was ambiguous are not included.

For the 3N1 lineage, most inferences at low and intermediate

nesting levels are dispersal restricted by distance. There is evidence

for recent range expansion into the narrow southeastern portion of

its range, where this lineage coexists with EA6 sexuals. The oldest

inference is a range expansion from west to east, corresponding to

the initial expansion following their formation in the west via

hybridization between the CA6 and SM6 sexuals. This event is

dated at 0.24 MYA (range 0.025–0.73 MYA). The fact that lower

and intermediate nesting levels have signatures of dispersal

restricted by distance, including in the central and eastern parts

of the range, suggests that any continuing range expansion is

relatively slow. However, it does still appear to be occurring at the

eastern edge of the range, as evidenced by the inference of range

expansion to the southeast in clade 1–1 (Table 2). There is also

evidence at the highest nesting level for fragmentation between

eastern and western 3N1 populations.

In the 3N2 lineage, there are inferences of dispersal restricted by

distance at both lower and intermediate nesting levels. However,

there are also several inferences of range expansion at multiple

nesting levels. These inferences suggest that 3N2 parthenogens

were formed in roughly the southern or western portion of their

current range approximately 0.07 MYA (range 0.006–0.22 MYA)

and have been spreading, and are continuing to spread, to the

north and east.

Dates for origins of the 3N1 and 3N2 lineages based solely on

their mtDNA divergence from the mostly closely related sampled

CA6 and SM6 mtDNA haplotypes, respectively, are 2.65 MYA for

3N1’s (range 0.54–6.67 MYA) and 1.21 MYA for 3N2’s (range

0.21–3.20 MYA), suggesting that they are older than their

respective earliest NCPA inferences. Dates for NCPA-inferred

initial range expansions are lower bounds for the ages of each

event, because dating is based on the youngest monophyletic clade

of the haplotype network for which the inference of range

expansion applies [20]. However, the limited mtDNA diversity

Table 1. Summary sequence information for each mtDNAlineage.

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Lineage/Region N#Haplotypes % Nuc Div (SE) Nesting Clades

3N1 Total 230 43 0.170 (0.065)

3N1 East Central 48 12 0.085 (0.040) 1-1, 1-2, 1-4 (2-1, 2-2)

3N1 Far West 17 2 0.034 (0.025) 1-4, 1-5 (2-2)

3N1 Northeast 26 4 0.029 (0.018) 1-1, 1-2 (2-1)

3N1 Northwest 10 2 0.047 (0.027) 1-2, 1-4 (2-1, 2-2)

3N1 Southeast 21 3 0.104 (0.057) 1-1, 1-2, 1-4 (2-1, 2-2)

3N1 Southwest 55 16 0.078 (0.029) 1-3, 1-4, 1-6, 1-8, 1-9 (2-2)

3N1 West Central 52 13 0.062 (0.027) 1-4, 1-7 (2-2)

3N2 Total 89 16 0.135 (0.050)

3N2 Central 27 4 0.090 (0.051) 1-2, 1-3, 1-6 (2-1, 2-3)

3N2 Far West 22 6 0.078 (0.037) 1-2, 1-3 (2-1)

3N2 Northeast 15 7 0.203 (0.067) 1-4, 1-5, 1-6, 1-7 (2-2, 2-3)

3N2 Southeast 24 4 0.110 (0.055) 1-1, 1-2, 1-3, 1-6 (2-1, 2-3)

NCPA nesting clade names correspond to those in Figures 1 and 2 for the 3N1and 3N2 lineages, respectively.doi:10.1371/journal.pone.0000760.t001..

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Asexual Gecko Phylogeography


within each lineage makes it very unlikely that they are as old as

their divergence from related sexual haplotypes would suggest. A

selective sweep within each group is an unlikely explanation for

this limited diversity because nuclear backcross clonal diversity is

much higher (16, and Strasburg and Kearney in prep), and since

the cytoplasmic and nuclear genomes are in complete linkage in

these parthenogens, a sweep in one would affect the other as well.

The most likely explanation is that more closely related sexual

haplotypes were not sampled. This is a plausible explanation

because regional divergence among CA6 and SM6 mtDNA

populations can be as much as 4–5% and 7–8%, respectively [29].

Minimum divergence between 3N1 and CA6 haplotypes is 4–5%,

and between 3N2 and SM6 haplotypes it is 2–3%.

Effective MigrationThe validity of NCPA for inferring population structure and

historical events has been questioned [32,33]. Although many of

these criticisms have been rebutted [34], the inherent uncertainty

in any such analysis warrants multiple alternative methods of

inference. Consequently, we have also implemented coalescent-

based analyses of effective migration in addition to more

traditional distance-based analyses.

Results from coalescent-based migration analyses are shown in

Table 3. Effective migration rate estimates generally had very

large confidence intervals, with lower ends of those intervals often

far below 0.1, suggesting that a high degree of subdivision among

these particular regions cannot be rejected. Only effective

migration rate estimates with confidence intervals completely

above 0.1 are considered significant.

Effective migration results for 3N1 strongly support NCPA

inferences of formation in the western portion of the range and

spread to the east and southeast. All significant migration occurs

within the western portion of the range or from west to east; no

migration was inferred out of the southeast or from east to west.

This highly asymmetric migration includes significant migration

inferred from most other regions, and from the Northwest region

in particular, to the Southeast region, where NCPA inferred

a recent and possibly continuing range expansion event.

While it is clear from NCPA and from phylogenetic relation-

ships between 3N1 and CA6 haplotypes that 3N1 parthenogens

originated in the western portion of their range, neither analysis

offers a more precise estimate of location. These migration

analyses suggest that the most likely location of origin is the

northern part of the western portion of the range (the Northwest

region). There has been asymmetric migration from this region to

the far western part of the range, and to the east and southeast,

with no evidence of significant migration into the Northwest

region.

Migration analysis of 3N2 is also concordant with NCPA

inferences, which suggested an origin in the southern or western

portion of their range and subsequent spread to the north and east.

There has been significant migration from western regions to the

southeast, and migration from the southeast to the northeast.

Mismatch Distributions, Analyses of Molecular

VarianceOther evidence for population growth can be obtained from an

examination of the distributions of pairwise differences among

Figure 1. Haplotype network for the 3N1 mtDNA lineage, showing nesting levels. Clades correspond to those listed in Table 2. Small ovals withoutletter names are haplotypes not sampled but which are necessary to connect sampled haplotypes. Pie charts next to each haplotype indicate theproportion of individuals with that haplotype sampled from the various regions described in the analytical methods and Figure 7.doi:10.1371/journal.pone.0000760.g001



Figure 2. Haplotype network for the 3N2 mtDNA lineage, showing nesting levels. Clades correspond to those listed in Table 2. Small ovals withoutletter names are haplotypes not sampled but which are necessary to connect sampled haplotypes. Pie charts next to each haplotype indicate theproportion of individuals with that haplotype sampled from the various regions described in the analytical methods and Figure 7.doi:10.1371/journal.pone.0000760.g002

Table 2. Results of NCPA for each mtDNA lineage.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Clone Clade Chain of Inference Result Age (MYA) C.I. (MYA)

3N1 1-1 1-2-3-5-6-TOO FEW-7-YES DRD in eastern part of range, range expansion to southeast 0.06 0.001–0.23

1-2 1-2-3-4-NO DRD in east/central part of range

1-4 1-2-3-4-NO DRD in western and central parts of range

2-1 1-2-3-4-NO DRD in eastern part of range

2-2 1-2-3-4-NO DRD in western and central parts of range

Total 1-19-20-2-11-12-13-YES Range expansion from west to east, possibly followed by somefragmentation between east and west

0.24 0.025–0.73

- - Age based on divergence from CA6 mtDNA 2.65 0.54–6.67

3N2 1-2 1-2-3-4-NO DRD in west and central

1-3 1-2-11-12-NO Range expansion from west/central to north/west parts of range 0.06 0.001–0.23

1-6 1-2-11-12-NO Range expansion from south/central to north/east parts of range 0.06 0.001–0.23

2-1 1-2-3-4-NO DRD throughout most of range

2-3 1-2-3-4-NO DRD in central, northern, and eastern parts of range

Total 1-2-11-12-NO Range expansion from west/central to north/east parts of range 0.07 0.006-0.22

- - Age based on divergence from SM6 mtDNA 1.21 0.21-3.20

Only clades with significant values are shown. DRD = dispersal restricted by distance. For dates and confidence intervals, point estimates are based on an estimate of1.3% sequence divergence per million years for this portion of the mtDNA genome. 95% Confidence intervals are based on a gamma probability distribution forcoalescence time and a range of 1.22-1.4% divergence per million years (see methods). MYA = million years ago.doi:10.1371/journal.pone.0000760.t002....

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haplotypes, or mismatch distributions [35,36]. Populations that

have undergone or are undergoing periods of growth tend to have

a unimodal distribution of pairwise differences, with the mode

shifting to the right with time following growth. Conversely, stable

populations tend to show multimodal ‘‘ragged’’ mismatch

distributions [37].

Graphs of mismatch distributions for each mtDNA lineage, and

for eastern and western portions of the 3N1 range, are shown in

Figure 3. In each case, the distribution is clearly unimodal or

bimodal. The 3N2 clone has a strongly unimodal mismatch

distribution; the estimate of t, time since expansion measured in

units of 1/(2u) generations [where u is total substitution rate over

Table 3. Effective migration rates (average number of effective migrants per generation) among regions within each mtDNAlineage.

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Clone/Source Region Receiving Region

3N1 East Central Far West Northeast Northwest Southeast Southwest West Central

East Central - 0–0.14 0.42–2.13 0–0.07 0.05–4.06 0–0.65 0–0.55

Far West 0–0.17 - 0–0.34 0.05–0.21 0.69–7.17 4.99–11.36 0–0.55

Northeast 0–0.17 0–0.14 - 0–0.03 1.15–8.57 0–0.65 0–0.55

Northwest 0–0.17 1.64–3.30 0–0.34 - 5.93–18.60 0–0.65 3.55–8.57

Southeast 0–0.17 0–0.14 0–0.34 0.03–0.17 - 0–0.65 0–0.55

Southwest 0.01–0.39 0–0.14 0–0.34 0–0.03 0–1.77 - 0–0.55

West Central 0–0.17 0–0.14 0–0.34 0–0.03 0.05–4.06 0–0.65 -

3N2 Central Far West Northeast Southeast

Central - 1.27–4.19 0–0.68 0–0.22

Far West 0–0.19 - 0–0.68 0.14–1.06

Northeast 0 0 - 0

Southeast 0.03–0.51 0–0.43 1.06–4.79 -

Values shown are 95% confidence intervals for Nefm = effective migration rate = inbreeding effective population size times proportion of individuals migrating. Directionof migration is from the region listed at left to the region listed across the top. Significant migration events (defined as those estimates whose confidence intervals arecompletely above 0.1) are shown in bold italics.doi:10.1371/journal.pone.0000760.t003..

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Figure 3. Mismatch distributions for 3N1 and 3N2 lineages. Distributions are shown for all 3N1 or 3N2 populations together and for eastern andwestern 3N1 populations.doi:10.1371/journal.pone.0000760.g003



all sites–38], based on the least-squares method implemented in

Arlequin is 1.82, and a sudden expansion model cannot be

rejected for this data. Based on our substitution rate estimate of

0.65% per lineage per million years, this corresponds to a timing of

approximately 0.11 MYA for the initial expansion of 3N2’s

following formation. This time is very consistent with the estimate

made based on the NCPA inference of northward and eastward

expansion (0.07 MYA, range 0.006–0.22 MYA). In addition,

estimates of Tajima’s [39] D and Fu’s [40] Fs were significantly

negative, indicating population expansion.

The overall 3N1 distribution shows two peaks, at one and five

differences. The peak at five differences corresponds to a timing of

approximately 0.30 MYA, which coincides well with the timing of

the initial expansion event inferred by NCPA (0.24 MYA, range

0.025–0.73 MYA). The peak at one difference corresponds to

a timing of approximately 0.06 MYA, which is the same time as

the estimate for range expansion to the southeast portion of the

range inferred by NCPA (0.06 MYA, range 0.001–0.23 MYA).

Further examination of Figure 3 reveals that this bimodality is due

to eastern 3N1 individuals, while western 3N1 individuals show

a unimodal mismatch distribution. This bimodality is likely the

result of contraction/fragmentation during the LGM and sub-

sequent continued expansion to the east and south.

The estimate of t for western 3N1 is 1.04, corresponding to 0.06

MYA for this expansion-considerably more recent than estimates

for eastern expansion. Based on a variety of evidence (NCPA,

effective migration, affinity of 3N1 mtDNA with western CA6

mtDNA, and affinity of many 3N1 chromosomal and allozyme

variants with western CA6 and SM6 variants–16), it is clear that

the 3N1 mtDNA clone originated in western Australia. Therefore,

this expansion in western 3N1 may also reflect Holocene

expansion following contraction during the LGM. Eastern,

western, and overall 3N1 fit a sudden expansion model of

population growth. Tajima’s D is significantly negative for western

and overall 3N1 (western D = 22.38, p,0.0001; overall

D = 21.81, p = 0.006), and Fu’s Fs is significantly negative for

all three groups (eastern Fs = 26.57, p = 0.009; western Fs,2100,

p,0.0001; overall Fs = 210.65, p = 0.002).

Results from AMOVA of mtDNA for each lineage are shown in

Table 4. Groups for AMOVAs are the same regions that were

used for effective migration analyses. For both mtDNA lineages,

among region, within region, and within population comparisons

all explain a significant portion of genetic variation. However, the

distribution of variation is quite different between the two lineages.

Relatively little variation is distributed among regions in the 3N2

lineage, and most of the remaining variation is found within

populations; this is consistent with the more recent origin of the

3N2’s and their comparatively small range. In the 3N1 lineage,

more than two thirds of the variation is distributed among regions.

However, in an AMOVA with eastern and western populations as

the groups, an almost identical amount of variation (66.5%) is

distributed between groups, suggesting that almost all of this

regional variation is distributed between eastern and western

populations. This is consistent with the NCPA inference of

a possible relatively old fragmentation event between eastern and

western populations. Within regions, almost all variation is found

within rather than among populations.

Mantel tests [41] of correlation between geographic distance

and genetic distance were performed on each mtDNA lineage as

a whole and within the 3N1 mtDNA lineage for eastern and

western populations separately. For all tests, there is a significant

correlation between geographic and genetic distance. In the 3N1’s,

the correlation was lowest (but still significant) among western

populations (western 3N1 r = 0.19, p = 0.049; eastern 3N1

r = 0.47, p = 0.002; overall 3N1 r = 0.53, p,0.0001; 3N2

r = 0.30, p = 0.003). Mantel tests were also run on the same

regions used in previous analyses, but almost all results were not

significant even if correlation coefficients were high, most likely

due to small sample sizes.

Distribution ModelingKearney et al. [24] found that the current distribution of

parthenogenetic H. binoei coincides fairly closely with their

expected distribution based on correlations with six temperature

and rainfall variables in western Australia, while considerable

similar but unoccupied habitat exists in central and eastern

Australia. Taking a more mechanistic approach, Kearney and

Porter [25] found that the current southern distribution of the H.

binoei complex is partially limited by temperature requirements for

successful egg development and foraging activity. Here we have

applied these approaches using estimates of climatic conditions

during the LGM. Average air temperatures in the interior of

Australia were around 9uC cooler 16–45 KYA than at present

[42]. The arid zone was also considerably drier during the LGM,

although estimates of the degree of aridification vary [31].

Predicted correlative distribution models for parthenogenetic H.

binoei and biophysical predictions for the temperature limits for

successful egg developments and minimal foraging activity are

shown in Figure 4. Three scenarios are presented, with mean

annual rainfall reductions of 1/2, 1/3, and 1/4 (all with a 9uCaverage temperature reduction).

Probability of occurrence based on correlations with tempera-

ture and rainfall variables decreases dramatically throughout

much of the interior of Australia under all three scenarios;

probability density is shifted to southeastern and southwestern

Australia, where rainfall amounts are similar to current levels in

the interior. However, the 9uC temperature decrease shifts the

contours for biophysical predictions of minimal temperatures for

successful egg development and foraging far to the north. Under

our assumptions of temperature and rainfall conditions during the

LGM, and assuming that climatic correlates and biophysical

requirements of current H. binoei are comparable to those of the

LGM, the regions where they were most likely to persist during the

LGM were the northwest and north-central parts of the arid zone.

DISCUSSION

Phylogeographic History of H. binoei ParthenogensNCPA of the 3N1 and 3N2 parthenogens reveal a recent origin of

each lineage and subsequent spread to the east and south (3N1)

and east and north (3N2). Dating estimates of the oldest NCPA

inferences, which correspond to initial expansion following

Table 4. AMOVA results for mtDNA sequence.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Race Source d. f. Sum Sq. % Variation P

3N1 Among Regions 6 195.471 66.75 ,0.00001

Among Pops 34 24.751 3.30 0.00131

Within Pops 188 85.909 29.95 ,0.00001

Total 228 306.131

3N2 Among Regions 3 13.844 13.80 0.00875

Among Pops 14 24.250 28.42 ,0.00001

Within Pops 70 36.474 57.78 ,0.00001

Total 87 74.568

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formation, put the 3N1 expansion at 0.24 MYA (range 0.025–0.73

MYA), and the 3N2 expansion at 0.07 MYA (range 0.006–0.22

MYA). As mentioned above, these dates for NCPA inferences of

initial range expansion are based on the youngest monophyletic

clade of the haplotype network for which the inference of range

expansion applies [20], which in each case corresponds to one or

more of the highest level nesting clades; thus these range

expansion dates set lower bounds for the ages of each lineage.

There is no evidence suggesting that there would have been any

substantial delay between formation and range expansion in either

lineage, and we expect dates for formation to be close to dates for

initial range expansion. Analyses of effective migration support

northwestern and southwestern or central-western origins for the

3N1 and 3N2 lineages, respectively. NCPA and effective migration

also indicate recent and possibly ongoing expansions to the

southeast in 3N1’s and to the east in 3N2’s, and mismatch

distributions also suggest rapid population growth in each lineage.

Coalescent analyses of effective population growth show that

overall, and in most regions, populations of both parthenogens are

growing very quickly, as would be expected under a scenario of

recent and rapid range expansion (data not shown).

There is also evidence at the highest NCPA nesting level for

fragmentation between eastern and western 3N1 populations. In

addition, AMOVA using the eastern and western areas as groups

reveals a large amount (66%) of variation distributed between

groups, and eastern and western haplotypes are mostly segregated

at the highest levels of nesting in the haplotype network (Figure 1).

However, analyses of effective migration (Table 3) provide no

evidence of east/west fragmentation; in fact, there is strong signal

of west to east migration. Sampling in the middle portion of the

3N1 range is somewhat sparse in comparison to more eastern and

western areas, and this may be partially responsible for an

inference of fragmentation; more sampling in the region may

reveal intermediate haplotypes and more continuity between east

and west. While this fragmentation inference may be considered

slightly tentative, it is interesting that the predicted distribution of

parthenogenetic H. binoei during the LGM under 33% and 50%

rainfall reduction scenarios is somewhat discontinuous in this

region (Figure 4), with an area of low probability of occurrence,

corresponding roughly with the fragmentation event, separating

two areas of higher probability of occurrence (see ‘‘Distribution

Modeling’’ below).

Based on the mtDNA restriction profiles showing an affinity of

3N2 mtDNA with a clade of SM6 haplotypes from the extreme

western part of their range along the northwest coast of Australia,

Moritz and Heideman [27] concluded that the 3N2 mtDNA

lineage had likely originated in the northwestern part of its range

(see Figure 2 in 26). Under this scenario, 3N2 parthenogens then

spread to the east and south to occupy their current range.

However, based on our mtDNA sequence data [29] this SM6

clade also includes a haplotype sampled from near Shark Bay at

the west-central edge of the 3N2 range. No other SM6 individuals

were sampled within 400 km of this population (see Figure 2, 29),

so it could well be a remnant population from a more southern

Figure 4. Statistical distribution models for parthenogenetic Heteronotia binoei. Models are for (a) present conditions and (b–d) last glacialmaximum conditions with a 9uC reduction in mean air temperature and three different rainfall reduction scenarios. All statistical models are based onthe AICc model reported in Kearney et al (2003). Overlayed on the predicted distributions are the contours for biophysical predictions of the limit forsuccessful development of eggs (600 degree days), of the limit for potential activity time (0 hours) and of the number of hours of potential activity atthe current distribution limit of the Heteronotia complex (400 hours). Any regions roughly south of the contours are outside the fundamental niche ofHeteronotia. The biophysical predictions use either (a) current climatic conditions or (b–d) a 9uC decrease in monthly maximum and minimumtemperatures. Methods for biophysical predictions are described in Kearney and Porter (2004).doi:10.1371/journal.pone.0000760.g004



historical distribution of this race. It seems likely that the CA6

and/or SM6 ranges in this area have changed substantially due to

Pleistocene climatic changes (see below), and these range shifts

facilitated hybridization between ecologically and genetically

distinct races in this group [29,43]. This, combined with NCPA

and effective migration analyses showing range expansion and

movement to the east and north in the 3N2 lineage, make it most

likely that the 3N2 parthenogens were formed in the west-central

or southwest part of their current range.

Distribution ModelingModeling of the climatic correlates of H. binoei distributions and

biophysical modeling of limits for successful egg development and

minimal foraging time strengthen our phylogeographic scenario.

Kearney et al. [24] analyzed the bioclimatic envelopes of each

asexual lineage and found that six climatic variables related to

temperature and rainfall fairly accurately describe the distributions

of each lineage in the western parts of their ranges, but that in each

case large areas of climatically similar habitat exist to the east of

their current ranges (see Figure 9 in 24). This result is in

agreement with our inference of recent and continuing eastward

expansion within each lineage. Concordance between the

predicted 3N1 range based on these climatic variables and our

inferred range expansion is especially striking–an uninhabitable

area in the Lake Eyre basin around northeast South Australia,

southeast Northern Territory, and southwest Queensland is mostly

surrounded by more suitable habitat (see Figure 9 in 24), and the

southern part of this circle corresponds to the recent southeastern

range expansion and our predicted continuing expansions

(Figure 5).

It is significant that the 3N1 lineage has not expanded further

into the southwestern part of Australia, an area where no H. binoei

exist. Kearney and Porter [25] showed that in many places the

southern limit of the range of the EA6 sexual chromosome race

(the most southerly distributed chromosome race) coincides very

closely with the thermal limit for successful egg development;

similar climatic constraints on the 3N1 southern distribution are

likely to be in place.

We repeated these correlative analyses for both parthenogenetic

lineages combined, under current climatic conditions as well as

under three different scenarios for the LGM–a uniform 9uCdecrease in average air temperature along with rainfall reductions

of 25%, 33%, and 50% (Figure 4). During the LGM, rainfall

conditions similar to those in present-day parthenogenetic H. binoei

ranges would mostly have been restricted to extreme southwestern

and southeastern Australia. Rainfall was strongly weighted in the

correlative distribution model for parthenogenetic Heteronotia

(Kearney et al. 2003) hence the prediction for a significant

southward shift in the distribution.

We have also extended the biophysical modeling of Kearney

and Porter [25] to include these LGM scenarios (overlaid contour

lines on Figure 4b–d for the 600 degree days necessary for

Figure 5. Proposed origin and spread of 3N1 and 3N2 parthenogens. Also shown are timing estimates for expansions and hypothesized futureexpansions in 3N1 parthenogens. Phylogeographic events are overlaid on the predicted distribution for parthenogenetic Heteronotia binoei based ona statistical distribution model for present climatic conditions [24]. Times given here are point estimates; confidence intervals are given in Table 2.DRD = dispersal restricted by distance.doi:10.1371/journal.pone.0000760.g005



successful egg development and the zero and 400 hours potential

activity time contours). Correlative distribution model predictions

are discordant with those of the biophysical model, which shows

that most of the areas of highest probability density in the

correlative model are well south of the 600 degree day and zero

hours potential activity contour lines, and so would likely have

been outside the fundamental niche of H. binoei based on these

biophysical requirements [25]. Regions of most similar habitat

north of the contour lines are found in the northwest and north-

central parts of the arid zone, and for the 33% and 50% rainfall

reduction scenarios they are separated by an area of somewhat less

similar habitat. The absence of extremely cold and arid

environments in Australia at present is presumably why extrap-

olation of the regression model results in a biologically unrealistic

prediction.

It is particularly interesting to note that the biophysical model

predicts potential activity time to be more limiting than potential

egg development time during glacial maxima, whereas the reverse

is true under current climatic conditions. This occurs because egg

development rate in the soil is affected by solar radiation and air

temperature, while potential activity time in this nocturnal lizard is

solely affected by air temperature. Potential activity time is more

severely affected because our modeling assumes that the air

temperature changes between glacial cycles but solar radiation

does not. In this respect, it may be significant that parthenogenetic

H. binoei have evolved greater aerobic endurance at low

temperature when compared with their sexual relatives [44].

Concordance between phylogeographic analyses

and distribution modelingWhile our modeling for the LGM is somewhat crude in that it

assumes geographically uniform changes in temperature and

rainfall (probably not a realistic assumption–31), it is in substantial

agreement with our phylogeographic results, summarized in

Figure 5. We have inferred an origin of the 3N1 mitochondrial

lineage approximately 240,000 years ago, likely during the

previous glacial cycle, in the northwest part of its range. This

would have been near the southern limit of the fundamental niche

of the Heteronotia complex (assuming roughly similar conditions

during the glacial maximum previous to the LGM), and it is

reasonable to expect that the CA6 and SM6 sexual races would

have come into contact in this region as the range of each was

contracted northward. Following some, mostly eastward, expan-

sion, the 3N1 range contracted to the northwest and north-central

arid zone during the LGM, possibly into two disjunct regions (see

Figure 4b–d). This is a likely cause of the fragmentation event

inferred at higher levels of nesting in the 3N1 NCPA. Also during

the LGM, the 3N2 parthenogens were formed via a second period

of contact and hybridization between the CA6 and SM6 races in

Western Australia. Under this scenario, the range of the SM6

sexuals during the LGM extended further to the south in this area,

and the population from Shark Bay is a remnant of this southern

range.

Results for both 3N1 and 3N2 lineages suggest that abiotic

factors may play the most important role in determining their

geographic distributions. However, it is worth pointing out that

both lineages appear to still be expanding their ranges, and so are

likely in a non-equilibrium state. In addition, Moritz et al. [45]

found much higher rates of infection by parasitic mites for

parthenogenetic H. binoei sampled throughout their range relative

to their sexual counterparts. Studies of the environmental and

physiological tolerances of different parthenogenetic clones are

underway (Kearney and Strasburg in prep), and further studies

involving direct competition and transplant experiments will help

strengthen inferences of limiting factors in parthenogen distribu-

tions.

The Australian arid zone is home to a diverse array of hybrid

parthenogens [reviewed in 46], and those that have been studied

in detail also appear to have late Pleistocene origins [30,47]. Many

explanations have been put forth for the persistence of partheno-

gens in the arid zone and elsewhere [9,10,48–50], and the role of

climatic cycling in hybridization is well-documented [51]. It may

be the case that similar climatic conditions have driven the

hybridization events resulting in other arid zone parthenogens,

and that similar factors constrain their distributions. We were able

to make robust inferences about the history of the H. binoei

complex in relation to climatic cycles by combining population

genetic approaches with climatic and biophysical distribution

modeling. This methodology should also be very valuable for

understanding the prevalence of hybrid parthenogenesis in the

Australian arid zone, and for addressing the role of abiotic factors

in the formation, spread, and persistence of parthenogenetic

lineages more generally.

MATERIALS AND METHODS

FieldOur analyses are based on 319 specimens of parthenogenetic H.

binoei, encompassing the ranges of the two mtDNA lineages known

as 3N1 and 3N2. Of these samples, 127 were collected in the

1980’s and early 1990’s [26] and 192 were collected in 2000–2001

(Table 5 and Figure 6). In some cases, nearby populations with

small population sizes were combined for analyses. For the 2001

collections, representative individuals from each population were

euthanized for voucher specimens, and for the rest tail tips were

taken and the individuals were released. Voucher specimens are

deposited in the South Australian Museum, Australian National

Wildlife Collection, Queensland Museum, and University of

Michigan Museum of Zoology (for individuals collected by C.

Moritz), and in the Western Australian Museum (for individuals

collected in 2001). Museum catalog numbers for voucher speci-

mens are given in Table 5.

MolecularTechniques for DNA extraction, amplification and sequencing are

described in Strasburg and Kearney [29]. We sequenced the ND2

(NADH dehydrogenase subunit two) gene and flanking tRNA

genes, a region particularly useful for intraspecific and intrageneric

studies because of its relatively high rate of evolution [52,53]. All

sequences have been submitted to GenBank, and accession

numbers are given in Table 5.

AnalyticalAMOVAs were performed for mtDNA sequence for both lineages

using the computer program Arlequin 2.001 [54]. Uncorrected

pairwise differences were used as the distance measure, and

significance was assessed with 16,000 permutations. Mantel tests

and mismatch analyses were also performed in Arlequin, with

10,000 permutations for Mantel tests and 1000 bootstrap

replicates for mismatch analyses. Nucleotide diversities were

calculated using Mega 2.1 [55], with standard errors calculated

using the bootstrap method with 1,000 resamples.

Nested clade phylogeographic analysis (NCPA–34) was per-

formed separately on the 3N1 and 3N2 lineages. Haplotype

networks were inferred under the criterion of statistical parsimony

[56] using TCS 1.16 [57], and permutations and significance tests

were performed using Geodis 2.0 [58] with 10,000 permutations.



Single-locus phylogeographic studies are typically limited by the

fact that they cannot account for inter-locus variability due to both

mutational and coalescent stochasticity. While we acknowledge the

former limitation with this study, the latter is not an issue here

because these geckos reproduce clonally.

Dating of NCPA inferences was performed using the method of

Templeton [20]. This method allows for calculation of a point

estimate for the age of a given event, and a confidence interval

around that estimate that accounts for evolutionary stochasticity

by modeling the distribution of time to coalescence as a gamma

function [59]. Point estimates were obtained by comparing

sequence diversity in the youngest monophyletic clade of the

haplotype network for which the inference applies and sequence

divergence from the nearest clade: divergence time t = (Dxy-

0.5*(Dx+Dy))*substitution rate, where Dxy is average divergence

between the focal clade and its neighboring clade, and Dx and Dy

are average diversity within each clade [60]. For the section of

mtDNA sequenced here, Macey et al. [61] estimated the rate of

evolution in Agamid lizards to be 0.65% per lineage per million

years (range based on geological dating estimates 0.61–0.70%),

corresponding to a divergence rate of 1.3% per million years.

Other studies have found highly concordant rates in other reptile,

amphibian, and fish taxa [62]. In our 95% confidence intervals,

we used a range of 0.61–0.70% per lineage per million years

(corresponding to 1.22–1.4% divergence per million years) to

account for some error in the estimate of evolutionary rate. In

order to verify our assumption of equal rates of evolution along

lineages for NCPA dating, a likelihood ratio test of a molecular

clock [63] was performed on a tree of all sexual and

parthenogenetic H. binoei haplotypes (including the EA6 sexual

chromosome race) rooted with a single H. planiceps haplotype. We

were unable to reject a molecular clock (2d = 285.234, df = 255,

p = 0.094; for details on maximum likelihood analysis conditions

see ref. 29).

Effective migration rates among populations and regions within

each race were measured using the computer program Migrate

1.7.6 [64]. Migrate uses a Markov chain Monte Carlo approach

with importance sampling [65] to estimate Nefm, where Nef is the

long-term inbreeding effective size and m is the average

proportion of individuals migrating per generation. Analyses were

run with 20 short chains with 1,000,000 genealogies sampled and

10,000 genealogies recorded, and 2 long chains with 10,000,000

genealogies sampled and 100,000 genealogies recorded. Analyses

in Migrate were run both with individual populations and with

nearby populations combined into regions to increase sample sizes

and for ease of interpretation. 3N1 populations were grouped into

Far West, Northwest, Southwest, West Central, East Central,

Northeast, and Southeast regions, and 3N2 populations were

grouped into Far West, Central, Northeast, and Southeast regions

(Figure 7). Populations were grouped by eye, and in a few cases

populations that were distant from any others were not included in

a region. Combining populations that may show some genetic

structure violates an assumption of the models underlying the

coalescent techniques used in these programs; however, this is

often a reasonable step to facilitate computation and interpretation

of analyses [66]. Summed results from individual populations were

very consistent with results from regions, suggesting that the

analyses are in fact quite robust to violations of this assumption.

Distribution modelingWe used two contrasting approaches to predict the distribution of

parthenogenetic H. binoei during current and LGM conditions:

a correlative approach and a mechanistic approach. The

correlative approach was based on a previously generated logistic

regression model using six climatic predictor variables including

mean annual temperature rainfall and humidity, as well as

temperature and rainfall variability [24]. Predictions were made

using current climatic conditions, as reported in Kearney et al.

[24], as well as estimated conditions during the LGM. These

estimates involve a 9uC reduction in mean annual air temperature

[42] and three scenarios of reduced mean annual rainfall (3/4, 2/3

Figure 6. Sampling localities for the 3N1 and 3N2 mitochondrial clones. Latitude/longitude data and sample sizes are given in Table 5. Ranges ofthe CA6, EA6, and SM6 sexual chromosome races are shown in light gray. The inset in the upper right shows the extent of the arid zone in dark gray.doi:10.1371/journal.pone.0000760.g006



Table 5. Sampling localities, sample sizes, and mtDNA haplotypes sampled at each locality.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

mtDNA Lineage

Locality Latitude Longitude 3N1 3N2 Museum Catalog NumbersGenBank AccessionNumbers Haplotypes

17kms S Leonora WA 229.033 121.317 1 ABTC32512 DQ287414 3N1-P

22km NE Bonnie Rock WA 230.383 118.500 1 ABTC31222 DQ287372 3N1-P

4km W Johnston Rocks WA 229.800 119.833 1 ABTC31220 DQ287371 3N1-P

Aileron NT 222.648 133.352 5 DQ287444, DQ287463,DQ287470, DQ287473,DQ287475

3N1-N (5)

Alice Springs NT 223.700 133.867 2 ABTC32565, ABTC32566 DQ287419, DQ287420 3N1-N (2)

Avoca Downs Station 230.950 122.318 4 R144987 DQ287495, DQ287544,DQ287550, DQ287589

3N1-AD (2), 3N1-AM(2)

Bandya WA 227.550 121.828 7 R146801 DQ287496, DQ287498,DQ287516, DQ287521,DQ287531, DQ287545,DQ287571

3N1-P (2), 3N1-T,3N1-Y, 3N1-Z (2),3N1-AN

Belele WA 226.367 118.017 5 ABTC32361, ABTC32373,ABTC32445, ABTC32457,ABTC32461

DQ287604, DQ287608,DQ287626, DQ287632,DQ287633

3N2-B (2), 3N2-D (3)

Billabalong WA 227.417 115.833 3 ABTC32371, ABTC32374,ABTC32385

DQ287607, DQ287609,DQ287613

3N2-D (2), 3N2-E

Billabong WA 226.817 114.617 4 ABTC32444, ABTC32526,ABTC32530, ABTC32542

DQ287625, DQ287641,DQ287643, DQ287647

3N2-D (2), 3N2-E (2)

Boologooroo WA 224.333 114.033 2 ABTC32378, ABTC32379 DQ287610, DQ287611 3N2-H (2)

Brickhouse WA 224.817 113.783 5 ABTC32796, ABTC32800,ABTC32916, ABTC33076,ABTC33078

DQ287655, DQ287656,DQ287667, DQ287673,DQ287674

3N2-B (2), 3N2-G (3)

Bulga Downs WA 228.494 119.739 5 R146770 DQ287560, DQ287563,DQ287570, DQ287575,DQ287576

3N1-P (5)

Bullabulling WA 231.017 120.867 3 ABTC32364, ABTC32470,ABTC32490

DQ287385, DQ287403,DQ287411

3N1-P, 3N1-AC (2)

Bullardoo Stn WA 227.850 115.667 2 2 ABTC32458, ABTC32471,ABTC32386, ABTC32447

DQ287400, DQ287404,DQ287614, DQ287628

3N1-P (2), 3N2-D (2)

Bundarra Stn WA 228.317 121.167 2 2 ABTC32504, ABTC32534,ABTC32507, ABTC32537

DQ287413, DQ287416,DQ287637, DQ287645

3N1-AB (2), 3N2-L(2)

Coondambo SA 231.060 135.865 3 ABTC32440 DQ287398, DQ287456,DQ287488

3N1-P (3)

Copper Hill SA 227.950 134.313 1 DQ287446 3N1-A

Cundeelee WA 230.720 123.422 11 R146757, R146792 DQ287494, DQ287517,DQ287526, DQ287553,DQ287557, DQ287558,DQ287569, DQ287572,DQ287586, DQ287587,DQ287597

3N1-P (5), 3N1-AA(2), 3N1-AM (4)

Cunyu WA 226.017 120.117 1 ABTC32404 DQ287387 3N1-P

Curbur WA 226.467 115.933 4 ABTC32354, ABTC32446,ABTC32450, ABTC32528

DQ287602, DQ287627,DQ287630, DQ287642

3N2-D (3), 3N2-F

Dalgetty Downs WA 225.283 116.200 5 ABTC32370, ABTC32387,ABTC33128, ABTC33130,ABTC33131

DQ287606, DQ287615,DQ287675, DQ287676,DQ287677

3N2-D (2), 3N2-F (3)

De Rose Hill SA 226.417 133.250 2 ABTC32431, ABTC32522 DQ287394, DQ287415 3N1-I, 3N1-L

Deep Well NT 224.298 134.133 4 DQ287465, DQ287468,DQ287471, DQ287600

3N1-I, 3N1-N (3)

Earaheedy WA 225.550 121.583 1 ABTC32397 DQ287386 3N1-P

East edge of Yeo Lake WA 228.236 124.673 1 DQ287542 3N1-P

East of Yeo WA 228.136 124.465 7 R146769, R146781, R146803,R146804, R146808

DQ287504, DQ287512,DQ287537, DQ287549,DQ287564, DQ287581,DQ287596

3N1-P (3), 3N1-Y (4)

Glenayle WA 225.267 122.033 1 ABTC31426 DQ287381 3N1-P

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mtDNA Lineage


Goongarrie WA 229.984 121.044 6 R145049 DQ287497, DQ287503,DQ287515, DQ287533,DQ287574, DQ287585

3N1-P (3), 3N1-AF(2), 3N1-AP

Granite Downs SA 226.937 133.495 5 ABTC32435, ABTC32486 DQ287396, DQ287409,DQ287452, DQ287486,DQ287490

3N1-E, 3N1-I, 3N1-N(3)

Granite Peak WA 225.633 121.350 1 ABTC32541 DQ287646 3N2-N

Hampton Hill WA 230.762 121.737 4 R146787 DQ287519, DQ287556,DQ287598, DQ287599

3N1-S (2), 3N1-AO,3N1-AP

Jimba Jimba WA 225.033 115.133 1 ABTC32930 DQ287670 3N2-B

Juna Downs WA 222.883 118.483 4 ABTC32773, ABTC32819,ABTC32820, ABTC32834

DQ287652, DQ287659,DQ287660, DQ287662

3N2-L (3), 3N2-O

Kathleen Valley WA 227.400 120.650 1 ABTC32509 DQ287638 3N2-L

Kingoonya SA 230.912 135.315 2 DQ287437, DQ287482 3N1-P (2)

Kirkalocka WA 228.555 117.777 1 2 ABTC32408, ABTC32417,ABTC32525

DQ287389, DQ287622,DQ287640

3N1-P, 3N2-L (2)

Lake Violet WA 226.533 120.667 2 ABTC32418, ABTC32421 DQ287390, DQ287392 3N1-P (2)

Leinster Downs WA 227.850 120.600 3 ABTC32349, ABTC32426,ABTC32505

DQ287601, DQ287624,DQ287636

3N2-L (3)

Lilla Ck. NT 225.567 134.067 1 ABTC31643 DQ287383 3N1-I

Mandilla WA 231.376 121.537 1 DQ287566 3N1-AJ

Mt Augusta WA 224.300 116.917 4 ABTC32847, ABTC32929,ABTC32932, ABTC32934

DQ287665, DQ287669,DQ287671, DQ287672

3N2-F (4)

Mt Cavenagh NT 225.915 133.133 5 DQ287432, DQ287433,DQ287443, DQ287462,DQ287476

3N1-I (5)

Mt Ebenezer NT 225.100 132.567 1 ABTC31384 DQ287378 3N1-J

Mt Gould WA 225.800 117.383 4 ABTC32358, ABTC32367,ABTC32390, ABTC32449

DQ287603, DQ287605,DQ287617, DQ287629

3N2-B (3), 3N2-F

Mt Willoughby SA 227.958 134.145 7 ABTC32436, ABTC32441,ABTC32469

DQ287397, DQ287399,DQ287402, DQ287449,DQ287454, DQ287458,DQ287474

3N1-E, 3N1-H (3),3N1-I, 3N1-N, 3N1-P

Munarra WA 226.283 118.700 2 4 ABTC31373, ABTC32472,ABTC32409, ABTC32413,ABTC32416, ABTC32477

DQ287376, DQ287405,DQ287618, DQ287620,DQ287621, DQ287635

3N1-N, 3N1-P, 3N2-B (4)

Nallan WA 227.317 117.967 4 ABTC32388, ABTC32412,ABTC32422, ABTC32467

DQ287616, DQ287619,DQ287623, DQ287634

3N2-C, 3N2-D (3)

NE of Yamarna WA 228.127 123.699 4 R146760 DQ287508, DQ287530,DQ287538, DQ287562

3N1-P, 3N1-Y (3)

Neale Junction WA 228.304 125.816 1 R146765 DQ287541 3N1-U

Neds Creek WA 225.483 119.650 1 ABTC32432 DQ287395 3N1-P

New Springs WA 225.817 120.000 2 ABTC32485, ABTC32544 DQ287408, DQ287418 3N1-P (2)

Ninghan WA 229.431 117.287 2 DQ287582, DQ287583 3N1-AK (2)

North Well SA 230.843 135.310 6 DQ287424, DQ287429,DQ287445, DQ287447,DQ287453, DQ287492

3N1-D, 3N1-N (2),3N1-P (3)

Oakden Hills SA 231.667 137.033 3 ABTC32427, ABTC32473,ABTC32535

DQ287393, DQ287406,DQ287417

3N1-P (3)

Old Andado NT 225.383 135.283 2 ABTC31374, ABTC31417 DQ287377, DQ287380 3N1-I, 3N1-N

Old Bandya WA 227.697 122.134 6 R146761 DQ287501, DQ287502,DQ287511, DQ287532,DQ287535, DQ287539

3N1-P (5), 3N1-AG

Orange Ck. #1 NT 224.450 133.450 1 ABTC31179 DQ287370 3N1-M

Orange Ck. #2 NT 223.997 133.592 5 DQ287440, DQ287448,DQ287455, DQ287459,DQ287467

3N1-F, 3N1-N (4)

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Table 5. Cont.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



mtDNA Lineage


Pack Saddle Camp WA 222.917 118.750 1 ABTC32839 DQ287663 3N2-L

Parallel Road #2 WA 226.998 125.545 2 R145133, R146785 DQ287559, DQ287584 3N1-AI (2)

Pinjin WA 230.080 122.732 6 R145007, R145086, R145140 DQ287518, DQ287523,DQ287524, DQ287529,DQ287543, DQ287548

3N1-P (4), 3N1-V,3N1-AM

Queen Victoria Springs WA 230.790 123.363 1 R146783 DQ287578 3N1-AM

Rocklea Stn WA 222.883 117.450 4 ABTC32771, ABTC32772,ABTC32774, ABTC32817

DQ287650, DQ287651,DQ287653, DQ287657

3N2-L, 3N2-M (3)

Ross River NT 223.597 134.485 2 DQ287421, DQ287426 3N1-I, 3N1-N

Sandstone WA 227.992 119.292 3 1 R146762, R146777 DQ287500, DQ287520,DQ287536, DQ287678

3N1-P (3), 3N2-L

Sherwood WA 226.567 118.533 4 ABTC32381, ABTC32453,ABTC32511, ABTC32532

DQ287612, DQ287631,DQ287639, DQ287644

3N2-L (4)

Sylvania WA 223.583 120.050 1 ABTC32770 DQ287649 3N2-I

The Garden NT 223.282 134.417 4 DQ287477, DQ287479,DQ287481, DQ287485

3N1-N (4)

Thundelarra WA 228.967 117.117 1 ABTC31352 DQ287375 3N1-P

Ti Tree NT 222.132 133.267 3 DQ287425, DQ287480,DQ287489

3N1-N (3)

Tieyon SA 226.208 133.855 4 DQ287435, DQ287442,DQ287461, DQ287487

3N1-E, 3N1-I, 3N1-N(2)

Uluru NT 225.417 131.967 1 ABTC31233 DQ287373 3N1-N

Umbeara NT 225.748 133.683 5 DQ287430, DQ287450,DQ287469, DQ287472,DQ287483

3N1-G, 3N1-I (2),3N1-K, 3N1-N

Uranerz Camp WA 230.159 123.443 1 R146782 DQ287522 3N1-W

Victory Downs SA 225.988 132.970 5 DQ287423, DQ287457,DQ287460, DQ287464,DQ287491

3N1-I (4), 3N1-N

Warakurna WA 225.033 128.250 1 ABTC31392 DQ287379 3N1-N

Welbourn Hill SA 227.357 134.085 6 ABTC31653 DQ287384, DQ287422,DQ287427, DQ287438,DQ287441, DQ287451

3N1-I (2), 3N1-N (4)

Wheelerrana Stn WA 223.983 120.000 5 ABTC32769, ABTC32778,ABTC32818, ABTC32821,ABTC32925

DQ287648, DQ287654,DQ287658, DQ287661,DQ287668

3N2-J, 3N2-K, 3N2-P(3)

White Cliffs WA 228.479 122.803 10 R145139, R146802 DQ287510, DQ287513,DQ287525, DQ287540,DQ287546, DQ287552,DQ287555, DQ287567,DQ287591, DQ287595

3N1-P (5), 3N1-X,3N1-AE (4)

Windsor WA 228.012 118.580 2 5 R146775 DQ287514, DQ287534,DQ287683, DQ287684,DQ287685, DQ287688,DQ287689

3N1-P (2), 3N2-L (5)

Wintinna SA 227.712 134.115 3 DQ287434, DQ287466,DQ287484

3N1-B, 3N1-C, 3N1-I

Wirraminna SA 231.190 136.228 7 ABTC31429, ABTC32495 DQ287382, DQ287412,DQ287428, DQ287431,DQ287436, DQ287439,DQ287478

3N1-N (3), 3N1-P (4)

Woomarel WA 225.733 114.283 1 ABTC32849 DQ287666 3N2-D

Yamarna WA 228.161 123.668 4 R145083, R146771, R146784,R146800

DQ287493, DQ287547,DQ287551, DQ287565

3N1-P, 3N1-R, 3N1-U, 3N1-AH

Yarri WA 229.777 122.359 7 R145005, R145061 DQ287505, DQ287528,DQ287573, DQ287577,DQ287580, DQ287592,DQ287594

3N1-O, 3N1-P (5),3N1-AL

Yellowdine WA 231.300 119.650 1 ABTC32488 DQ287410 3N1-P....

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Table 5. Continued.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



and 1/2 of current rainfall levels), with the other four climatic

variables held constant. We used a range of rainfall reduction

scenarios because there is considerable uncertainty in this respect

(31 and P. Hope pers. comm.).

The mechanistic approach involved applying biophysical

models to predict regions where egg development and above-

ground activity are possible. This approach provides a means to

map the fundamental niche of an organism (see [25] and [67] for

details). Previous research has shown that H. binoei require

approximately 600 degree days above 20uC for successful egg

development, and that these lizards rarely forage at air

temperatures below 15uC. Biophysical predictions were made

using current climatic conditions, as reported in Kearney and

Porter (2004), as well as an inferred 9uC reduction in monthly

maximum and minimum air temperature during the LGM [42].

We assume here that habitat preferences for parthenogenetic H.

binoei have not changed significantly since the LGM. While there

are physiological differences between parthenogenetic and sexual

H. binoei [44] which may have been a consequence of

hybridization or evolved post-hybridization, there are no obvious

mtDNA Lineage


Yeo WA 228.077 124.318 5 R145047, R146766, R146772,R146791, R146796

DQ287499, DQ287509,DQ287527, DQ287590,DQ287593

3N1-P (5)

Yindi WA 230.384 122.507 6 R145006, R145059 DQ287506, DQ287507,DQ287554, DQ287568,DQ287579, DQ287588

3N1-P (5), 3N1-Q

Yoothapinna WA 226.533 118.517 1 ABTC32846 DQ287664 3N2-L

Yowergabbie WA 228.242 117.661 1 6 DQ287561, DQ287679,DQ287680, DQ287681,DQ287682, DQ287686,DQ287687

3N1-P, 3N2-A, 3N2-C (4), 3N2-D

Yundamindra WA 229.250 122.100 2 ABTC32407, ABTC32480 DQ287388, DQ287407 3N1-P (2)

Yunndaga WA 229.783 121.150 2 ABTC32420, ABTC32466 DQ287391, DQ287401 3N1-P, 3N1-AQ

TOTALS 230 89

Haplotype names correspond to those in Figures 1 and 2. Sampling sites are shown in Figure 6. Museum catalog numbers beginning with ABTC refer to SouthAustralian Museum, and those beginning with R refer to Western Australian Museum.doi:10.1371/journal.pone.0000760.t005..

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.

Table 5. Continued.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Figure 7. Regions used for various analyses. Ranges for each mtDNA lineage as a whole are shown in light gray.doi:10.1371/journal.pone.0000760.g007



differences in how they use microhabitats–both shelter and lay

their eggs under a wide variety of surface debris as well as in

burrows.

ACKNOWLEDGMENTSPaul Doughty, Geordie Torr, Jane Melville, Dale Roberts, Dave

O’Conner, and Ben Phillips provided essential expert help with field

collecting. We would like to thank Jane Melville for her generous financial

and logistical assistance during field work, and the many station owners

throughout Australia who gave us permission to collect on their properties.

MK specimens were collected under permits S24357 1 (South Australia)

and 8758 (Northern Territory) and exported from Australia under permit

WT 2002-1168.

Author Contributions

Conceived and designed the experiments: JS MK. Performed the

experiments: JS MK. Analyzed the data: JS MK. Contributed reagents/

materials/analysis tools: CM AT JS MK. Wrote the paper: CM AT JS

MK.

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Combining Phylogeography with Distribution Modeling: Multiple Pleistocene Range Expansions in a Parthenogenetic Gecko from the Australian Arid Zone

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